arXiv:2406.19680v1 Announce Type: cross Abstract: In recent years, generative artificial intelligence has achieved significant advancements in the field of image generation, spawning a variety of applications. However, video generation still faces considerable challenges in various aspects, such as controllability, video length, and richness of details, which hinder the application and popularization of this technology. In this work, we propose a controllable video generation framework, dubbed MimicMotion, which can generate high-quality videos of arbitrary length mimicking specific motion guidance. Compared with previous methods, our approach has several highlights. Firstly, we introduce confidence-aware pose guidance that ensures high frame quality and temporal smoothness. Secondly, we introduce regional loss amplification based on pose confidence, which significantly reduces image distortion. Lastly, for generating long and smooth videos, we propose a progressive latent fusion strategy. By this means, we can produce videos of arbitrary length with acceptable resource consumption. With extensive experiments and user studies, MimicMotion demonstrates significant improvements over previous approaches in various aspects. Detailed results and comparisons are available on our project page: https://tencent.github.io/MimicMotion .
The article “MimicMotion: A Controllable Video Generation Framework” discusses the challenges faced in video generation and presents a novel framework called MimicMotion that addresses these challenges. While generative artificial intelligence has made significant advancements in image generation, video generation still lags behind due to issues such as controllability, video length, and richness of details. MimicMotion aims to overcome these limitations by introducing confidence-aware pose guidance, regional loss amplification, and a progressive latent fusion strategy. These features ensure high frame quality, temporal smoothness, reduced image distortion, and the ability to generate long and smooth videos with acceptable resource consumption. Through extensive experiments and user studies, MimicMotion demonstrates significant improvements over previous approaches.
The Future of Video Generation: Introducing MimicMotion
In recent years, the field of generative artificial intelligence has made remarkable strides in image generation, revolutionizing a wide range of applications. However, the same level of advancement has not yet been achieved in video generation. Video generation poses unique challenges such as controllability, video length, and richness of details, which have hindered the widespread adoption of this technology.
Fortunately, we now have a solution that addresses these challenges and paves the way for the future of video generation. Introducing MimicMotion, a groundbreaking controllable video generation framework.
Confidence-Aware Pose Guidance
One of the key innovations of MimicMotion is the introduction of confidence-aware pose guidance. This novel approach ensures high frame quality and temporal smoothness in the generated videos. By incorporating confidence measurements, our framework can generate videos that accurately mimic specific motion guidance, resulting in more realistic and visually appealing outputs.
Regional Loss Amplification
To further enhance the quality of generated videos, MimicMotion introduces regional loss amplification based on pose confidence. This technique significantly reduces image distortion, resulting in higher fidelity and more visually pleasing videos. By focusing on regions with higher confidence, we can preserve the details and fine nuances of the generated content.
Progressive Latent Fusion Strategy
Generating long and smooth videos has always been a challenging task. However, MimicMotion overcomes this limitation by introducing a progressive latent fusion strategy. This innovative approach allows us to produce videos of arbitrary length while maintaining acceptable resource consumption. Users can now enjoy seamless, uninterrupted videos without compromising on quality or performance.
With extensive experiments and user studies, MimicMotion has demonstrated significant improvements over previous approaches in various aspects of video generation. Our framework opens up new possibilities for applications in entertainment, virtual reality, and beyond.
To explore detailed results and comparisons, please visit our project page: https://tencent.github.io/MimicMotion. Here, you can witness the true potential of MimicMotion and witness the future of video generation.
Conclusion: MimicMotion is a pioneering framework that tackles the challenges of video generation head-on. With its confidence-aware pose guidance, regional loss amplification, and progressive latent fusion strategy, we can now generate high-quality videos of any length, mimicking specific motion guidance. The possibilities are endless, and the future of video generation just got brighter.
The paper, titled “MimicMotion: Controllable Video Generation with Confidence-Aware Pose Guidance,” addresses the challenges faced in video generation and proposes a novel framework to overcome them. While generative artificial intelligence has made significant strides in image generation, video generation has lagged behind due to issues related to controllability, video length, and richness of details. The authors of this paper aim to tackle these challenges and improve the application and popularization of video generation technology.
The proposed framework, MimicMotion, introduces several key features that set it apart from previous methods. Firstly, it incorporates confidence-aware pose guidance, which ensures high frame quality and temporal smoothness. By utilizing pose information, the generated videos can accurately mimic specific motion guidance, resulting in more realistic and controllable outputs.
Additionally, the authors introduce regional loss amplification based on pose confidence. This technique helps reduce image distortion, improving the overall visual quality of the generated videos. By focusing on areas with higher pose confidence, the framework can preserve important details and enhance the realism of the generated content.
Furthermore, the paper addresses the challenge of generating long and smooth videos by proposing a progressive latent fusion strategy. This strategy allows the framework to produce videos of arbitrary length while maintaining acceptable resource consumption. This is a crucial advancement, as previous methods often struggled with generating videos of extended duration without sacrificing quality or requiring excessive computational resources.
To validate the effectiveness of MimicMotion, the authors conducted extensive experiments and user studies. The results demonstrate significant improvements over previous approaches in various aspects, including controllability, video length, and richness of details. The authors have also provided detailed results and comparisons on their project page, offering further insights into the performance of their framework.
Overall, this paper presents a promising advancement in the field of video generation. By addressing key challenges and introducing innovative techniques, MimicMotion shows great potential for improving the quality and controllability of generated videos. Future research in this area could explore further advancements in controllable video generation, potentially incorporating additional factors such as audio guidance or multi-modal inputs to enhance the realism and richness of the generated content.
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